The exemplary embodiment relates to the classification arts and finds particular application in connection with a system and method for selecting images based on photographic style by learning photographic style annotations using a data-driven approach.
Photographic style refers to a consistent way of shooting photographic images which can be achieved by manipulating camera configurations, such as shutter speed, exposure, ISO level, and the like. In general, different styles are achieved changing how a particular subject is shot, rather than by repeatedly shooting the same subject. Styles can impact the viewer in different ways. For example, motion blur is perceived as an indication of speed, activity, and dynamism.
Graphic designers often have access to large databases of photographic images which may be annotated with semantic content-based tags, such as “sky,” “person,” or “flower.” While submitters of photographs to such creative databases are generally competent with regard to assigning content-based tags, they are often less able or willing to apply tags based on photographic style. Thus, when searching for an image which conveys a particular emotion or has a particular photographic style, the user is faced with searching through large numbers of images to find ones which meet the general criteria of interest.
Automated assessment of style has also proved difficult due to the shortage of such annotated data. Attributes of images have been selected in an attempt to make such assessments. Liu, et al., for example, uses three features (spectral slope, gradient histogram, and maximum saturation) to create feature vectors for training and classifying patches of an image as either blurry or non-blurry. A fourth feature, local auto-correlation, is used to classify each blurry patch further, as either an out-of-focus or a motion-type blur (Liu, et al., “Image partial blur detection and classification,” CVPR pp. 1-8, 2008). In Gemert, stylistic attributes such as colorfulness, lighting, depth of field, viewpoint, and salience, are assembled into a semantic categorization model (Gemert, “Exploiting photographic style for category-level image classification by generalizing the spatial pyramid,” ICMR, 14:1-14:8, 2011). Stöttinger, et al., describes features that may be used to retrieve images based on specific criteria, some of which relate to photographic style (Stöttinger, et al., “Translating journalists' requirements into features for image search,” VSMM, pp. 149-153, 2009). These include color (full color vs. black and white), shooting focus (related to depth-of-field and macro styles) and composition (in the sense of the degree of complexity of the image).
There remains a need for a system and method which provides a more comprehensive approach to assigning aspects of photographic style to an image and which provides a user-friendly interactive method for retrieving images based on these aspects.